



"""simple docstring"""







import itertools
import os
import random
import tempfile
import unittest

import numpy as np

from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
			import torch

if is_datasets_available():
			from datasets import load_dataset

UpperCamelCase     =							random.Random()

def       _lowerCamelCase				(							UpperCAmelCase_ :       Union[str, Any],	UpperCAmelCase_ :       Dict=1.0,	UpperCAmelCase_ :       List[Any]=None,	UpperCAmelCase_ :       Optional[Any]=None     )	-> int:






	"""simple docstring"""





	if rng is None:
		A__											=					global_rng

	A__											=					[]
	for batch_idx in range(shape[0]     ):
		values.append([]     )
		for _ in range(shape[1]     ):
			values[-1].append(rng.random() * scale     )

	return values






class       UpperCamelCase__			(   unittest.TestCase      ):

			"""simple docstring"""


			def __init__(      self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__=7						,	SCREAMING_SNAKE_CASE__=400						,	SCREAMING_SNAKE_CASE__=2000						,	SCREAMING_SNAKE_CASE__=2048						,	SCREAMING_SNAKE_CASE__=128						,	SCREAMING_SNAKE_CASE__=1						,	SCREAMING_SNAKE_CASE__=512						,	SCREAMING_SNAKE_CASE__=30						,	SCREAMING_SNAKE_CASE__=44100						,	)      ->	Optional[Any]:
				A__											=					parent
				A__											=					batch_size
				A__											=					min_seq_length
				A__											=					max_seq_length
				A__											=					(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
				A__											=					spectrogram_length
				A__											=					feature_size
				A__											=					num_audio_channels
				A__											=					hop_length
				A__											=					chunk_length
				A__											=					sampling_rate


			def   snake_case__	(      self		)      ->	int:
				return {
				    "spectrogram_length": self.spectrogram_length,
				    "feature_size": self.feature_size,
				    "num_audio_channels": self.num_audio_channels,
				    "hop_length": self.hop_length,
				    "chunk_length": self.chunk_length,
				    "sampling_rate": self.sampling_rate,
				}







			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__=False						,	SCREAMING_SNAKE_CASE__=False		)      ->	Tuple:
				def _flatten(SCREAMING_SNAKE_CASE__		):
					return list(itertools.chain(*SCREAMING_SNAKE_CASE__		)		)

				if equal_length:
					A__											=					[floats_list((self.max_seq_length, self.feature_size)		) for _ in range(self.batch_size		)]
				else:
					# make sure that inputs increase in size
					A__											=					[
					    floats_list((x, self.feature_size)		)
					    for x in range(self.min_seq_length						,	self.max_seq_length						,	self.seq_length_diff		)
					]
				if numpify:
					A__											=					[np.asarray(SCREAMING_SNAKE_CASE__		) for x in speech_inputs]
				return speech_inputs






@require_torch
@require_torchaudio
class       UpperCamelCase__			(   _lowerCAmelCase       ,		unittest.TestCase      ):

			"""simple docstring"""


			A__	:					str							  =     TvltFeatureExtractor


			def   snake_case__	(      self		)      ->	str:
				A__											=					TvltFeatureExtractionTester(self		)


			def   snake_case__	(      self		)      ->	Dict:
				A__											=					self.feature_extraction_class(**self.feat_extract_dict		)
				self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__						,	"spectrogram_length"		)		)
				self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__						,	"feature_size"		)		)
				self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__						,	"num_audio_channels"		)		)
				self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__						,	"hop_length"		)		)
				self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__						,	"chunk_length"		)		)
				self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__						,	"sampling_rate"		)		)


			def   snake_case__	(      self		)      ->	List[str]:
				A__											=					self.feature_extraction_class(**self.feat_extract_dict		)

				with tempfile.TemporaryDirectory() as tmpdirname:
					A__											=					feat_extract_first.save_pretrained(SCREAMING_SNAKE_CASE__		)[0]
					check_json_file_has_correct_format(SCREAMING_SNAKE_CASE__		)
					A__											=					self.feature_extraction_class.from_pretrained(SCREAMING_SNAKE_CASE__		)

				A__											=					feat_extract_first.to_dict()
				A__											=					feat_extract_second.to_dict()
				A__											=					dict_first.pop("mel_filters"		)
				A__											=					dict_second.pop("mel_filters"		)
				self.assertTrue(np.allclose(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)		)
				self.assertEqual(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)


			def   snake_case__	(      self		)      ->	Tuple:
				A__											=					self.feature_extraction_class(**self.feat_extract_dict		)

				with tempfile.TemporaryDirectory() as tmpdirname:
					A__											=					os.path.join(SCREAMING_SNAKE_CASE__						,	"feat_extract.json"		)
					feat_extract_first.to_json_file(SCREAMING_SNAKE_CASE__		)
					A__											=					self.feature_extraction_class.from_json_file(SCREAMING_SNAKE_CASE__		)

				A__											=					feat_extract_first.to_dict()
				A__											=					feat_extract_second.to_dict()
				A__											=					dict_first.pop("mel_filters"		)
				A__											=					dict_second.pop("mel_filters"		)
				self.assertTrue(np.allclose(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)		)
				self.assertEqual(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)


			def   snake_case__	(      self		)      ->	str:
				# Initialize feature_extractor
				A__											=					self.feature_extraction_class(**self.feat_extract_dict		)

				# create three inputs of length 800, 1000, and 1200
				A__											=					[floats_list((1, x)		)[0] for x in range(800						,	1400						,	200		)]
				A__											=					[np.asarray(SCREAMING_SNAKE_CASE__		) for speech_input in speech_inputs]

				# Test not batched input
				A__											=					feature_extractor(np_speech_inputs[0]						,	return_tensors="np"						,	sampling_rate=44100		).audio_values

				self.assertTrue(encoded_audios.ndim == 4		)
				self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size		)
				self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length		)
				self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels		)

				# Test batched
				A__											=					feature_extractor(SCREAMING_SNAKE_CASE__						,	return_tensors="np"						,	sampling_rate=44100		).audio_values

				self.assertTrue(encoded_audios.ndim == 4		)
				self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size		)
				self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length		)
				self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels		)

				# Test audio masking
				A__											=					feature_extractor(
				    SCREAMING_SNAKE_CASE__						,	return_tensors="np"						,	sampling_rate=44100						,	mask_audio=SCREAMING_SNAKE_CASE__		).audio_values

				self.assertTrue(encoded_audios.ndim == 4		)
				self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size		)
				self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length		)
				self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels		)

				# Test 2-D numpy arrays are batched.
				A__											=					[floats_list((1, x)		)[0] for x in (800, 800, 800)]
				A__											=					np.asarray(SCREAMING_SNAKE_CASE__		)
				A__											=					feature_extractor(SCREAMING_SNAKE_CASE__						,	return_tensors="np"						,	sampling_rate=44100		).audio_values
				self.assertTrue(encoded_audios.ndim == 4		)
				self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size		)
				self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length		)
				self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels		)


			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__		)      ->	Tuple:
				A__											=					load_dataset("hf-internal-testing/librispeech_asr_dummy"						,	"clean"						,	split="validation"		)
				# automatic decoding with librispeech
				A__											=					ds.sort("id"		).select(range(SCREAMING_SNAKE_CASE__		)		)[:num_samples]["audio"]

				return [x["array"] for x in speech_samples]







			def   snake_case__	(      self		)      ->	Optional[int]:
				A__											=					self._load_datasamples(1		)
				A__											=					TvltFeatureExtractor()
				A__											=					feature_extractor(SCREAMING_SNAKE_CASE__						,	return_tensors="pt"		).audio_values

				self.assertEquals(audio_values.shape						,	(1, 1, 192, 128)		)

				A__											=					torch.tensor([[-0.3_0_3_2, -0.2_7_0_8], [-0.4_4_3_4, -0.4_0_0_7]]		)
				self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2]						,	SCREAMING_SNAKE_CASE__						,	atol=1e-4		)		)





def 							UpperCAmelCase		(       lowercase				,     lowercase				):







				"""simple docstring"""



				__lowercase					     =				word.split()

				def justify(lowercase				,     lowercase				,     lowercase				) -> str:
								__lowercase					     =				max_width - width
								__lowercase					     =				len(lowercase				)
								if len(lowercase				) == 1:
												# if there is only word in line
												# just insert overall_spaces_count for the remainder of line
												return line[0] + " " * overall_spaces_count
								else:
												__lowercase					     =				words_count - 1
												# num_spaces_between_words_list[i] : tells you to insert
												# num_spaces_between_words_list[i] spaces
												# after word on line[i]
												__lowercase					     =				spaces_to_insert_between_words * [
												    overall_spaces_count // spaces_to_insert_between_words
												]
												__lowercase					     =				(
												    overall_spaces_count % spaces_to_insert_between_words
												)
												# distribute spaces via round robin to the left words
												for i in range(lowercase				):
																num_spaces_between_words_list[i] += 1
												__lowercase					     =				[]
												for i in range(lowercase				):
																# add the word
																aligned_words_list.append(line[i]				)
																# add the spaces to insert
																aligned_words_list.append(num_spaces_between_words_list[i] * ''' '''				)
												# just add the last word to the sentence
												aligned_words_list.append(line[-1]				)
												# join the aligned words list to form a justified line
												return "".join(lowercase				)

				__lowercase					     =				[]
				__lowercase					     =				[]
				__lowercase					     =				0
				for word in words:
								if width + len(lowercase				) + len(lowercase				) <= max_width:
												# keep adding words until we can fill out max_width
												# width = sum of length of all words (without overall_spaces_count)
												# len(word) = length of current word
												# len(line) = number of overall_spaces_count to insert between words
												line.append(lowercase				)
												width += len(lowercase				)
								else:
												# justify the line and add it to result
												answer.append(justify(lowercase				,     lowercase				,     lowercase				)				)
												# reset new line and new width
												__lowercase     ,	__lowercase					     =				[word], len(lowercase				)
				__lowercase					     =				max_width - width - len(lowercase				)
				answer.append(''' '''.join(lowercase				) + (remaining_spaces + 1) * ''' '''				)
				return answer


if __name__ == "__main__":
		from doctest import testmod

		testmod()